Dec
3
To Transform or Not To Transform
Many of the forecasting packages in R requires a time series that is covariance stationary. For those who are not familiar with this term, there is an excellent online textbook by Hyndman and Athanasopoulos, Forecasting: Principles and Practice. Click here to go directly to that chapter.
The first step, therefore, in making a predictive or analytic procedure is to analyze a time series to see if it is already covariance stationary. More often than not, a transformation is required to convert the non-stationary time series to a stationary time series.
Examples of time series, include stock prices, raw material prices, and ALL data that is ordered by a given interval in date and time.
The first step, therefore, in making a predictive or analytic procedure is to analyze a time series to see if it is already covariance stationary. More often than not, a transformation is required to convert the non-stationary time series to a stationary time series.
Examples of time series, include stock prices, raw material prices, and ALL data that is ordered by a given interval in date and time.
